Object Detection Part 1
My goal in this series is to deploy a neural network capable of identifying and localizing pedestrians in an image (the combination of image classification and localization is called object detection). In the first part of the series I will do this by downloading a pretrained model and using transfer learning to fine tune it for my problem. In the second part, I will try to create and deploy a model from scratch.
A Perfectly Cromulent Intro to Simpson's Paradox
In this post, we will go over one of my favorite statistical phenomenons, Simpson's paradox, using interactive data modules.
You've Got Mail: Building a Spam Filter with R
We take an unorthodox approach to building a spam filter in R, using k-means clustering to filter out spam. The first half will go over the theory behind spam filters and clustering algorithms. The second half will go over what I did to build a spam filter with R.
Into the Woods: Visualizing Random Forests with R
You've probably heard random forest models described as "black boxes," models that show an input and an output and nothing in between. In this post, we go over techniques to show what a random forest model is doing, to make it less of a black box.
Web Scraping with R
I go over how to use R to harvest information from web pages. This post chronicles my use of rvest to harvest movie information from Rotten Tomatoes to explore the difference between professional critics and general audiences.